# Papers

Die folgenden sind die neuesten Papers (sortiert nach Veröffentlichungsdatum) zum Thema Prompt-Engineering für große Sprachmodelle (Large Language Models, LLMS). Wir aktualisieren die Liste der Papers täglich/wochentlich.

## Überblicke

- [Prompt Design and Engineering: Introduction and Advanced Methods](https://arxiv.org/abs/2401.14423) (Januar 2024)
- [A Survey on Hallucination in Large Language Models: Principles,Taxonomy, Challenges, and Open Questions](https://arxiv.org/abs/2311.05232) (November 2023)
- [An RL Perspective on RLHF, Prompting, and Beyond](https://arxiv.org/abs/2310.06147) (Oktober 2023)
- [Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation](https://arxiv.org/abs/2305.16938) (Mai 2023)
- [Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study](https://arxiv.org/abs/2305.13860) (Mai 2023)
- [Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond](https://arxiv.org/abs/2304.13712) (April 2023)
- [Tool Learning with Foundation Models](https://arxiv.org/abs/2304.08354) (April 2023)
- [One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era](https://arxiv.org/abs/2304.06488) (April 2023)
- [A Bibliometric Review of Large Language Models Research from 2017 to 2023](https://arxiv.org/abs/2304.02020) (April 2023)
- [A Survey of Large Language Models](https://arxiv.org/abs/2303.18223) (April 2023)
- [Nature Language Reasoning, A Survey](https://arxiv.org/abs/2303.14725) (März 2023)
- [Augmented Language Models: a Survey](https://arxiv.org/abs/2302.07842) (Februar 2023)
- [A Survey for In-context Learning](https://arxiv.org/abs/2301.00234) (Dezember 2022)
- [Towards Reasoning in Large Language Models: A Survey](https://arxiv.org/abs/2212.10403) (Dezember 2022)
- [Reasoning with Language Model Prompting: A Survey](https://arxiv.org/abs/2212.09597) (Dezember 2022)
- [Emergent Abilities of Large Language Models](https://arxiv.org/abs/2206.07682) (Juni 2022)
- [A Taxonomy of Prompt Modifiers for Text-To-Image Generation](https://arxiv.org/abs/2204.13988) (April 2022)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://arxiv.org/abs/2107.13586) (Juli 2021)

## Ansätze

- [Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4](https://arxiv.org/abs/2312.16171v1) (Dezember 2023)
- [Walking Down the Memory Maze: Beyond Context Limit through Interactive Reading](https://arxiv.org/abs/2310.05029) (Oktober 2023)
- [Large Language Models as Analogical Reasoners](https://arxiv.org/abs/2310.01714) (Oktober 2023)
- [LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models](https://arxiv.org/abs/2310.05736) (Oktober 2023)
- [Query-Dependent Prompt Evaluation and Optimization with Offline Inverse RL](https://arxiv.org/abs/2309.06653) (September 2023)
- [Chain-of-Verification Reduces Hallucination in Large Language Models](https://arxiv.org/abs/2309.11495) (September 2023)
- [Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers](https://arxiv.org/abs/2309.08532) (September 2023)
- [From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting](https://arxiv.org/abs/2309.04269) (September 2023)
- [Re-Reading Improves Reasoning in Language Models](https://arxiv.org/abs/2309.06275) (September 2023)
- [Graph of Thoughts: Solving Elaborate Problems with Large Language Models](https://arxiv.org/abs/2308.09687v2) (August 2023)
- [Skeleton-of-Thought: Large Language Models Can Do Parallel Decoding](https://arxiv.org/abs/2307.15337) (Juli 2023)
- [Focused Prefix Tuning for Controllable Text Generation](https://arxiv.org/abs/2306.00369) (Juni 2023)
- [Exploring Lottery Prompts for Pre-trained Language Models](https://arxiv.org/abs/2305.19500) (Mai 2023)
- [Less Likely Brainstorming: Using Language Models to Generate Alternative Hypotheses](https://arxiv.org/abs/2305.19339) (Mai 2023)
- [Let's Verify Step by Step](https://arxiv.org/abs/2305.20050) (Mai 2023)
- [Universality and Limitations of Prompt Tuning](https://arxiv.org/abs/2305.18787) (Mai 2023)
- [MultiTool-CoT: GPT-3 Can Use Multiple External Tools with Chain of Thought Prompting](https://arxiv.org/abs/2305.16896) (Mai 2023)
- [PEARL: Prompting Large Language Models to Plan and Execute Actions Over Long Documents](https://arxiv.org/abs/2305.14564v1) (Mai 2023)
- [Reasoning with Language Model is Planning with World Model](https://arxiv.org/abs/2305.14992v1) (Mai 2023)
- [Self-Critique Prompting with Large Language Models for Inductive Instructions](https://arxiv.org/abs/2305.13733) (Mai 2023)
- [Better Zero-Shot Reasoning with Self-Adaptive Prompting](https://arxiv.org/abs/2305.14106) (Mai 2023)
- [Hierarchical Prompting Assists Large Language Model on Web Navigation](https://arxiv.org/abs/2305.14257) (Mai 2023)
- [Interactive Natural Language Processing](https://arxiv.org/abs/2305.13246) (Mai 2023)
- [Can We Edit Factual Knowledge by In-Context Learning?](https://arxiv.org/abs/2305.12740) (Mai 2023)
- [In-Context Learning of Large Language Models Explained as Kernel Regression](https://arxiv.org/abs/2305.12766) (Mai 2023)
- [Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models](https://arxiv.org/abs/2305.04091v3) (Mai 2023)
- [Meta-in-context learning in large language models](https://arxiv.org/abs/2305.12907) (Mai 2023)
- [Let's Sample Step by Step: Adaptive-Consistency for Efficient Reasoning with LLMs](https://arxiv.org/abs/2305.11860) (Mai 2023)
- [Post Hoc Explanations of Language Models Can Improve Language Models](https://arxiv.org/abs/2305.11426) (Mai 2023)
- [Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Prompt](https://arxiv.org/abs/2305.11186) (Mai 2023)
- [TreePrompt: Learning to Compose Tree Prompts for Explainable Visual Grounding](https://arxiv.org/abs/2305.11497) (Mai 2023)
- [TELeR: A General Taxonomy of LLM Prompts for Benchmarking Complex Tasks](https://arxiv.org/abs/2305.11430) (Mai 2023)
- [Efficient Prompting via Dynamic In-Context Learning](https://arxiv.org/abs/2305.11170) (Mai 2023)
- [The Web Can Be Your Oyster for Improving Large Language Models](https://arxiv.org/abs/2305.10998) (Mai 2023)
- [Flatness-Aware Prompt Selection Improves Accuracy and Sample Efficiency](https://arxiv.org/abs/2305.10713) (Mai 2023)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://arxiv.org/abs/2305.10601) (Mai 2023)
- [ZeroPrompt: Streaming Acoustic Encoders are Zero-Shot Masked LMs](https://arxiv.org/abs/2305.10649) (Mai 2023)
- [Chain-of-Symbol Prompting Elicits Planning in Large Langauge Models](https://arxiv.org/abs/2305.10276) (Mai 2023)
- [CooK: Empowering General-Purpose Language Models with Modular and Collaborative Knowledge](https://arxiv.org/abs/2305.09955) (Mai 2023)
- [What In-Context Learning "Learns" In-Context: Disentangling Task Recognition and Task Learning](https://arxiv.org/abs/2305.09731) (Mai 2023)
- [Reprompting: Automated Chain-of-Thought Prompt Inference Through Gibbs Sampling](https://arxiv.org/abs/2305.09993) (Mai 2023)
- [Satisfiability-Aided Language Models Using Declarative Prompting](https://arxiv.org/abs/2305.09656) (Mai 2023)
- [Pre-Training to Learn in Context](https://arxiv.org/abs/2305.09137) (Mai 2023)
- [Boosted Prompt Ensembles for Large Language Models](https://arxiv.org/abs/2304.05970) (April 2023)
- [Global Prompt Cell: A Portable Control Module for Effective Prompt](https://arxiv.org/abs/2304.05642) (April 2023)
- [Why think step-by-step? Reasoning emerges from the locality of experience](https://arxiv.org/abs/2304.03843) (April 2023)
- [Revisiting Automated Prompting: Are We Actually Doing Better?](https://arxiv.org/abs/2304.03609) (April 2023)
- [REFINER: Reasoning Feedback on Intermediate Representations](https://arxiv.org/abs/2304.01904) (April 2023)
- [Reflexion: an autonomous agent with dynamic memory and self-reflection](https://arxiv.org/abs/2303.11366) (März 2023)
- [CAMEL: Communicative Agents for "Mind" Exploration of Large Scale Language Model Society](https://arxiv.org/abs/2303.17760) (März 2023)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://arxiv.org/abs/2303.17651v1) (März 2023)
- [kNN Prompting: Beyond-Context Learning with Calibration-Free Nearest Neighbor Inference](https://arxiv.org/abs/2303.13824) (März 2023)
- [Visual-Language Prompt Tuning with Knowledge-guided Context Optimization](https://arxiv.org/abs/2303.13283) (März 2023)
- [Fairness-guided Few-shot Prompting for Large Language Models](https://arxiv.org/abs/2303.13217) (März 2023)
- [Context-faithful Prompting for Large Language Models](https://arxiv.org/abs/2303.11315) (März 2023)
- [Is Prompt All You Need? No. A Comprehensive and Broader View of Instruction Learning](https://arxiv.org/abs/2303.10475) (März 2023)
- [UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation](https://arxiv.org/abs/2303.08518) (März 2023)
- [Model-tuning Via Prompts Makes NLP Models Adversarially Robust](https://arxiv.org/abs/2303.07320) (März 2023)
- [Structure Pretraining and Prompt Tuning for Knowledge Graph Transfer](https://arxiv.org/abs/2303.03922) (März 2023)
- [CoTEVer: Chain of Thought Prompting Annotation Toolkit for Explanation Verification](https://arxiv.org/abs/2303.03628) (März 2023)
- [Larger language models do in-context learning differently](https://arxiv.org/abs/2303.03846) (März 2023)
- [OpenICL: An Open-Source Framework for In-context Learning](https://arxiv.org/abs/2303.02913) (März 2023)
- [Dynamic Prompting: A Unified Framework for Prompt Tuning](https://arxiv.org/abs/2303.02909) (März 2023)
- [ART: Automatic multi-step reasoning and tool-use for large language models](https://arxiv.org/abs/2303.09014) (März 2023)
- [Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learning](https://arxiv.org/abs/2303.02861) (März 2023)
- [Effectiveness of Data Augmentation for Prefix Tuning with Limited Data](https://arxiv.org/abs/2303.02577) (März 2023)
- [Mixture of Soft Prompts for Controllable Data Generation](https://arxiv.org/abs/2303.01580) (März 2023)
- [Prompt, Generate, then Cache: Cascade of Foundation Models makes Strong Few-shot Learners](https://arxiv.org/abs/2303.02151) (März 2023)
- [How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Tasks](https://arxiv.org/abs/2303.00293) (März 2023)
- [Can ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-tuned BERT](https://arxiv.org/pdf/2302.10198.pdf) (Februar 2023)
- [EvoPrompting: Language Models for Code-Level Neural Architecture Search](https://arxiv.org/abs/2302.14838) (Februar 2023)
- [In-Context Instruction Learning](https://arxiv.org/abs/2302.14691) (Februar 2023)
- [Chain of Hindsight Aligns Language Models with Feedback](https://arxiv.org/abs/2302.02676) (Februar 2023)
- [Language Is Not All You Need: Aligning Perception with Language Models](https://arxiv.org/abs/2302.14045) (Februar 2023)
- [Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data](https://arxiv.org/abs/2302.12822) (Februar 2023)
- [Active Prompting with Chain-of-Thought for Large Language Models](https://arxiv.org/abs/2302.12246) (Februar 2023)
- [More than you've asked for: A Comprehensive Analysis of Novel Prompt Injection Threats to Application-Integrated Large Language Models](https://arxiv.org/abs/2302.12173) (Februar 2023)
- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://arxiv.org/abs/2302.11382) (Februar 2023)
- [Guiding Large Language Models via Directional Stimulus Prompting](https://arxiv.org/abs/2302.11520) (Februar 2023)
- [How Does In-Context Learning Help Prompt Tuning?](https://arxiv.org/abs/2302.11521) (Februar 2023)
- [Scalable Prompt Generation for Semi-supervised Learning with Language Models](https://arxiv.org/abs/2302.09236) (Februar 2023)
- [Bounding the Capabilities of Large Language Models in Open Text Generation with Prompt Constraints](https://arxiv.org/abs/2302.09185) (Februar 2023)
- [À-la-carte Prompt Tuning (APT): Combining Distinct Data Via Composable Prompting](https://arxiv.org/abs/2302.07994) (Februar 2023)
- [GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks](https://arxiv.org/abs/2302.08043) (Februar 2023)
- [The Capacity for Moral Self-Correction in Large Language Models](https://arxiv.org/abs/2302.07459) (Februar 2023)
- [SwitchPrompt: Learning Domain-Specific Gated Soft Prompts for Classification in Low-Resource Domains](https://arxiv.org/abs/2302.06868) (Februar 2023)
- [Evaluating the Robustness of Discrete Prompts](https://arxiv.org/abs/2302.05619) (Februar 2023)
- [Compositional Exemplars for In-context Learning](https://arxiv.org/abs/2302.05698) (Februar 2023)
- [Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery](https://arxiv.org/abs/2302.03668) (Februar 2023)
- [Multimodal Chain-of-Thought Reasoning in Language Models](https://arxiv.org/abs/2302.00923) (Februar 2023)
- [Large Language Models Can Be Easily Distracted by Irrelevant Context](https://arxiv.org/abs/2302.00093) (Februar 2023)
- [Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models](https://arxiv.org/abs/2302.00618) (Februar 2023)
- [Progressive Prompts: Continual Learning for Language Models](https://arxiv.org/abs/2301.12314) (Januar 2023)
- [Batch Prompting: Efficient Inference with LLM APIs](https://arxiv.org/abs/2301.08721) (Januar 2023)
- [Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP](https://arxiv.org/abs/2212.14024) (Dezember 2022)
- [On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning](https://arxiv.org/abs/2212.08061) (Dezember 2022)
- [Constitutional AI: Harmlessness from AI Feedback](https://arxiv.org/abs/2212.08073) (Dezember 2022)
- [Successive Prompting for Decomposing Complex Questions](https://arxiv.org/abs/2212.04092) (Dezember 2022)
- [Large Language Models are reasoners with Self-Verification](https://arxiv.org/abs/2212.09561v1) (Dezember 2022)
- [Discovering Language Model Behaviors with Model-Written Evaluations](https://arxiv.org/abs/2212.09251) (Dezember 2022)
- [Structured Prompting: Scaling In-Context Learning to 1,000 Examples](https://arxiv.org/abs/2212.06713) (Dezember 2022)
- [PAL: Program-aided Language Models](https://arxiv.org/abs/2211.10435) (November 2022)
- [Large Language Models Are Human-Level Prompt Engineers](https://arxiv.org/abs/2211.01910) (November 2022)
- [Ignore Previous Prompt: Attack Techniques For Language Models](https://arxiv.org/abs/2211.09527) (November 2022)
- [Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods](https://arxiv.org/abs/2210.07321) (November 2022)
- [Teaching Algorithmic Reasoning via In-context Learning](https://arxiv.org/abs/2211.09066) (November 2022)
- [Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference](https://arxiv.org/abs/2211.11875) (November 2022)
- [Ask Me Anything: A simple strategy for prompting language models](https://paperswithcode.com/paper/ask-me-anything-a-simple-strategy-for) (Oktober 2022)
- [Recitation-Augmented Language Models](https://arxiv.org/abs/2210.01296) (Oktober 2022)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://arxiv.org/abs/2210.03629) (Oktober 2022)
- [Prompting GPT-3 To Be Reliable](https://arxiv.org/abs/2210.09150) (Oktober 2022)
- [Decomposed Prompting: A Modular Approach for Solving Complex Tasks](https://arxiv.org/abs/2210.02406) (Oktober 2022)
- [Automatic Chain of Thought Prompting in Large Language Models](https://arxiv.org/abs/2210.03493) (Oktober 2022)
- [Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought](https://arxiv.org/abs/2210.01240v3) (Oktober 2022)
- [Evaluating the Susceptibility of Pre-Trained Language Models via Handcrafted Adversarial Examples](https://arxiv.org/abs/2209.02128) (September 2022)
- [Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning](https://arxiv.org/abs/2209.14610) (September 2022)
- [Promptagator: Few-shot Dense Retrieval From 8 Examples](https://arxiv.org/abs/2209.11755) (September 2022)
- [Atlas: Few-shot Learning with Retrieval Augmented Language Models](https://arxiv.org/abs/2208.03299) (November 2022)
- [DocPrompting: Generating Code by Retrieving the Docs](https://arxiv.org/abs/2207.05987) (Juli 2022)
- [On the Advance of Making Language Models Better Reasoners](https://arxiv.org/abs/2206.02336) (Juni 2022)
- [Large Language Models are Zero-Shot Reasoners](https://arxiv.org/abs/2205.11916) (Mai 2022)
- [Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations](https://arxiv.org/abs/2205.11822) (Mai 2022)
- [MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning](https://arxiv.org/abs/2205.00445) (Mai 2022)
- [PPT: Pre-trained Prompt Tuning for Few-shot Learning](https://aclanthology.org/2022.acl-long.576/) (Mqy 2022)
- [Toxicity Detection with Generative Prompt-based Inference](https://arxiv.org/abs/2205.12390) (Mai 2022)
- [Learning to Transfer Prompts for Text Generation](https://arxiv.org/abs/2205.01543) (Mai 2022)
- [The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning](https://arxiv.org/abs/2205.03401) (Mai 2022)
- [A Taxonomy of Prompt Modifiers for Text-To-Image Generation](https://arxiv.org/abs/2204.13988) (April 2022)
- [PromptChainer: Chaining Large Language Model Prompts through Visual Programming](https://arxiv.org/abs/2203.06566) (März 2022)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://arxiv.org/abs/2203.11171) (März 2022)
- [Training language models to follow instructions with human feedback](https://arxiv.org/abs/2203.02155)
- [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://arxiv.org/abs/2202.12837) (Februar 2022)
- [Chain of Thought Prompting Elicits Reasoning in Large Language Models](https://arxiv.org/abs/2201.11903) (Januar 2022)
- [Show Your Work: Scratchpads for Intermediate Computation with Language Models](https://arxiv.org/abs/2112.00114) (November 2021)
- [AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts](https://arxiv.org/abs/2110.01691) (Oktober 2021)
- [Generated Knowledge Prompting for Commonsense Reasoning](https://arxiv.org/abs/2110.08387) (Oktober 2021)
- [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://arxiv.org/abs/2110.08207) (Oktober 2021)
- [Reframing Instructional Prompts to GPTk's Language](https://arxiv.org/abs/2109.07830) (September 2021)
- [Design Guidelines for Prompt Engineering Text-to-Image Generative Models](https://arxiv.org/abs/2109.06977) (September 2021)
- [Making Pre-trained Language Models Better Few-shot Learners](https://aclanthology.org/2021.acl-long.295) (August 2021)
- [Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity](https://arxiv.org/abs/2104.08786) (April 2021)
- [BERTese: Learning to Speak to BERT](https://aclanthology.org/2021.eacl-main.316) (April 2021)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://arxiv.org/abs/2104.08691) (April 2021)
- [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://arxiv.org/abs/2102.07350) (Februar 2021)
- [Calibrate Before Use: Improving Few-Shot Performance of Language Models](https://arxiv.org/abs/2102.09690) (Februar 2021)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://arxiv.org/abs/2101.00190) (Januar 2021)
- [Learning to Generate Task-Specific Adapters from Task Description](https://arxiv.org/abs/2101.00420) (Januar 2021)
- [Making Pre-trained Language Models Better Few-shot Learners](https://arxiv.org/abs/2012.15723) (Dezember 2020)
- [Learning from Task Descriptions](https://aclanthology.org/2020.emnlp-main.105/) (November 2020)
- [AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts](https://arxiv.org/abs/2010.15980) (Oktober 2020)
- [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165) (Mai 2020)
- [How Can We Know What Language Models Know?](https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00324/96460/How-Can-We-Know-What-Language-Models-Know) (Juli 2020)
- [Scaling Laws for Neural Language Models](https://arxiv.org/abs/2001.08361) (Januar 2020)

## Anwendungen

- [PromptRE: Weakly-Supervised Document-Level Relation Extraction via Prompting-Based Data Programming](https://arxiv.org/abs/2310.09265) (Oktober 2023)
- [Prompting Large Language Models with Chain-of-Thought for Few-Shot Knowledge Base Question Generation](https://arxiv.org/abs/2310.08395) (Oktober 2023)
- [Who Wrote it and Why? Prompting Large-Language Models for Authorship Verification](https://arxiv.org/abs/2310.08123) (Oktober 2023)
- [Promptor: A Conversational and Autonomous Prompt Generation Agent for Intelligent Text Entry Techniques](https://arxiv.org/abs/2310.08101) (Oktober 2023)
- [Thought Propagation: An Analogical Approach to Complex Reasoning with Large Language Models](https://arxiv.org/abs/2310.03965) (Oktober 2023)
- [From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting](https://arxiv.org/abs/2309.04269) (September 2023)
- [Self-Taught Optimizer (STOP): Recursively Self-Improving Code Generation](https://arxiv.org/abs/2310.02304) (Oktober 2023)
- [Think before you speak: Training Language Models With Pause Tokens](https://arxiv.org/abs/2310.02226) (Oktober 2023)
- [(Dynamic) Prompting might be all you need to repair Compressed LLMs](https://arxiv.org/abs/2310.00867) (Oktober 2023)
- [In-Context Learning in Large Language Models: A Neuroscience-inspired Analysis of Representations](https://arxiv.org/abs/2310.00313) (September 2023)
- [Understanding In-Context Learning from Repetitions](https://arxiv.org/abs/2310.00297) (September 2023)
- [Investigating the Efficacy of Large Language Models in Reflective Assessment Methods through Chain of Thoughts Prompting](https://arxiv.org/abs/2310.00272) (September 2023)
- [Automatic Prompt Rewriting for Personalized Text Generation](https://arxiv.org/abs/2310.00152) (September 2023)
- [Efficient Streaming Language Models with Attention Sinks](https://arxiv.org/abs/2309.17453) (September 2023)
- [The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)](https://arxiv.org/abs/2309.17421) (September 2023)
- [Graph Neural Prompting with Large Language Models](https://arxiv.org/abs/2309.15427) (September 2023)
- [Large Language Model Alignment: A Survey](https://arxiv.org/abs/2309.15025) (September 2023)
- [Enhancing Zero-Shot Chain-of-Thought Reasoning in Large Language Models through Logic](https://arxiv.org/abs/2309.13339) (September 2023)
- [A Practical Survey on Zero-shot Prompt Design for In-context Learning](https://arxiv.org/abs/2309.13205) (September 2023)
- [EchoPrompt: Instructing the Model to Rephrase Queries for Improved In-context Learning](https://arxiv.org/abs/2309.10687) (September 2023)
- [Prompt, Condition, and Generate: Classification of Unsupported Claims with In-Context Learning](https://arxiv.org/abs/2309.10359) (September 2023)
- [PolicyGPT: Automated Analysis of Privacy Policies with Large Language Models](https://arxiv.org/abs/2309.10238) (September 2023)
- [LLM4Jobs: Unsupervised occupation extraction and standardization leveraging Large Language Models](https://arxiv.org/abs/2309.09708) (September 2023)
- [Summarization is (Almost) Dead](https://arxiv.org/abs/2309.09558) (September 2023)
- [Investigating Zero- and Few-shot Generalization in Fact Verification](https://arxiv.org/abs/2309.09444) (September 2023)
- [Performance of the Pre-Trained Large Language Model GPT-4 on Automated Short Answer Grading](https://arxiv.org/abs/2309.09338) (September 2023)
- [Contrastive Decoding Improves Reasoning in Large Language Models](https://arxiv.org/abs/2309.09117) (September 2023)
- [Struc-Bench: Are Large Language Models Really Good at Generating Complex Structured Data?](https://arxiv.org/abs/2309.08963) (September 2023)
- [Neural Machine Translation Models Can Learn to be Few-shot Learners](https://arxiv.org/abs/2309.08590) (September 2023)
- [Chain-of-Thought Reasoning is a Policy Improvement Operator](https://arxiv.org/abs/2309.08589) (September 2023)
- [ICLEF: In-Context Learning with Expert Feedback for Explainable Style Transfer](https://arxiv.org/abs/2309.08583) (September 2023)
- [When do Generative Query and Document Expansions Fail? A Comprehensive Study Across Methods, Retrievers, and Datasets](https://arxiv.org/abs/2309.08541) (September 2023)
- [Using Large Language Models for Knowledge Engineering (LLMKE): A Case Study on Wikidata](https://arxiv.org/abs/2309.08491) (September 2023)
- [Self-Consistent Narrative Prompts on Abductive Natural Language Inference](https://arxiv.org/abs/2309.08303) (September 2023)
- [Investigating Answerability of LLMs for Long-Form Question Answering](https://arxiv.org/abs/2309.08210) (September 2023)
- [PromptTTS++: Controlling Speaker Identity in Prompt-Based Text-to-Speech Using Natural Language Descriptions](https://arxiv.org/abs/2309.08140) (September 2023)
- [An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing](https://arxiv.org/abs/2309.08008) (September 2023)
- [Leveraging Contextual Information for Effective Entity Salience Detection](https://arxiv.org/abs/2309.07990) (September 2023)
- [Prompting4Debugging: Red-Teaming Text-to-Image Diffusion Models by Finding Problematic Prompts](https://arxiv.org/abs/2309.06135) (September 2023)
- [PACE: Prompting and Augmentation for Calibrated Confidence Estimation with GPT-4 in Cloud Incident Root Cause Analysis](https://arxiv.org/abs/2309.05833) (September 2023)
- [From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting](https://arxiv.org/abs/2309.04269) (September 2023)
- [Measuring and Improving Chain-of-Thought Reasoning in Vision-Language Models](https://arxiv.org/abs/2309.04461) (September 2023)
- [Zero-Resource Hallucination Prevention for Large Language Models](https://arxiv.org/abs/2309.02654) (September 2023)
- [Certifying LLM Safety against Adversarial Prompting](https://arxiv.org/abs/2309.02772) (September 2023)
- [Improving Code Generation by Dynamic Temperature Sampling](https://arxiv.org/abs/2309.02772) (September 2023)
- [Prompting a Large Language Model to Generate Diverse Motivational Messages: A Comparison with Human-Written Messages](https://arxiv.org/abs/2308.13479) (August 2023)
- [Financial News Analytics Using Fine-Tuned Llama 2 GPT Model](https://arxiv.org/abs/2308.13032) (August 2023)
- [A Study on Robustness and Reliability of Large Language Model Code Generation](https://arxiv.org/abs/2308.10335) (August 2023)
- [Large Language Models Vote: Prompting for Rare Disease Identification](https://arxiv.org/abs/2308.12890) (August 2023)
- [WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct](https://arxiv.org/abs/2308.09583) (August 2023)
- [Tree-of-Mixed-Thought: Combining Fast and Slow Thinking for Multi-hop Visual Reasoning](https://arxiv.org/abs/2308.09658) (August 2023)
- [Graph of Thoughts: Solving Elaborate Problems with Large Language Models](https://arxiv.org/abs/2308.09687) (August 2023)
- [Red-Teaming Large Language Models using Chain of Utterances for Safety-Alignment](https://arxiv.org/abs/2308.09662) (August 2023)
- [Boosting Logical Reasoning in Large Language Models through a New Framework: The Graph of Thought](https://arxiv.org/abs/2308.08614) (August 2023)
- [You Only Prompt Once: On the Capabilities of Prompt Learning on Large Language Models to Tackle Toxic Content](https://arxiv.org/abs/2308.05596) (August 2023)
- [LLM As DBA](https://arxiv.org/abs/2308.05481) (August 2023)
- [Interpretable Math Word Problem Solution Generation Via Step-by-step Planning](https://arxiv.org/abs/2306.00784) (Juni 2023)
- [In-Context Learning User Simulators for Task-Oriented Dialog Systems](https://arxiv.org/abs/2306.00774) (Juni 2023)
- [SQL-PaLM: Improved Large Language ModelAdaptation for Text-to-SQL](https://arxiv.org/abs/2306.00739) (Juni 2023)
- [Effective Structured Prompting by Meta-Learning and Representative Verbalizer](https://arxiv.org/abs/2306.00618) (Juni 2023)
- [Layout and Task Aware Instruction Prompt for Zero-shot Document Image Question Answering](https://arxiv.org/abs/2306.00526) (Juni 2023)
- [Chain-Of-Thought Prompting Under Streaming Batch: A Case Study](https://arxiv.org/abs/2306.00550) (Juni 2023)
- [Red Teaming Language Model Detectors with Language Models](https://arxiv.org/abs/2305.19713) (Mai 2023)
- [Gorilla: Large Language Model Connected with Massive APIs](https://shishirpatil.github.io/gorilla/) (Mai 2023)
- [Deliberate then Generate: Enhanced Prompting Framework for Text Generation](https://arxiv.org/abs/2305.19835) (Mai 2023)
- [What does the Failure to Reason with "Respectively" in Zero/Few-Shot Settings Tell Us about Language Models?](https://arxiv.org/abs/2305.19597) (Mai 2023)
- [ScoNe: Benchmarking Negation Reasoning in Language Models With Fine-Tuning and In-Context Learning](https://arxiv.org/abs/2305.19426) (Mai 2023)
- [SheetCopilot: Bringing Software Productivity to the Next Level through Large Language Models](https://arxiv.org/abs/2305.19308) (Mai 2023)
- [Grammar Prompting for Domain-Specific Language Generation with Large Language Models](https://arxiv.org/abs/2305.19234) (Mai 2023)
- [Mitigating Label Biases for In-context Learning](https://arxiv.org/abs/2305.19148) (Mai 2023)
- [Short Answer Grading Using One-shot Prompting and Text Similarity Scoring Model](https://arxiv.org/abs/2305.18638) (Mai 2023)
- [Strategic Reasoning with Language Models](https://arxiv.org/abs/2305.19165) (Mai 2023)
- [Dissecting Chain-of-Thought: A Study on Compositional In-Context Learning of MLPs](https://arxiv.org/abs/2305.18869) (Mai 2023)
- [Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models](https://arxiv.org/abs/2305.18189) (Mai 2023)
- [Leveraging Training Data in Few-Shot Prompting for Numerical Reasoning](https://arxiv.org/abs/2305.18170) (Mai 2023)
- [Exploring Effectiveness of GPT-3 in Grammatical Error Correction: A Study on Performance and Controllability in Prompt-Based Methods](https://arxiv.org/abs/2305.18156) (Mai 2023)
- [NOTABLE: Transferable Backdoor Attacks Against Prompt-based NLP Models](https://arxiv.org/abs/2305.17826) (Mai 2023)
- [Tab-CoT: Zero-shot Tabular Chain of Thought](https://arxiv.org/abs/2305.17812) (Mai 2023)
- [Evaluating GPT-3 Generated Explanations for Hateful Content Moderation](https://arxiv.org/abs/2305.17680) (Mai 2023)
- [Prompt-Guided Retrieval Augmentation for Non-Knowledge-Intensive Tasks](https://arxiv.org/abs/2305.17653) (Mai 2023)
- [Zero- and Few-Shot Event Detection via Prompt-Based Meta Learning]https://arxiv.org/abs/2305.17373) (Mai 2023)
- [Chain-of-Thought Hub: A Continuous Effort to Measure Large Language Models' Reasoning Performance](https://arxiv.org/abs/2305.17306) (Mai 2023)
- [Large Language Models Can be Lazy Learners: Analyze Shortcuts in In-Context Learning](https://arxiv.org/abs/2305.17256) (Mai 2023)
- [Heterogeneous Value Evaluation for Large Language Models](https://arxiv.org/abs/2305.17147) (Mai 2023)
- [PromptNER: Prompt Locating and Typing for Named Entity Recognition](https://arxiv.org/abs/2305.17104) (Mai 2023)
- [Small Language Models Improve Giants by Rewriting Their Outputs](https://arxiv.org/abs/2305.13514v1) (Mai 2023)
- [On the Planning Abilities of Large Language Models -- A Critical Investigation](https://arxiv.org/abs/2305.15771v1) (Mai 2023)
- [Beyond Chain-of-Thought, Effective Graph-of-Thought Reasoning in Large Language Models](https://arxiv.org/abs/2305.16582) (Mai 2023)
- [PRODIGY: Enabling In-context Learning Over Graphs](https://arxiv.org/abs/2305.12600v1) (Mai 2023)
- [Large Language Models are Few-Shot Health Learners](https://arxiv.org/abs/2305.15525v1) (Mai 2023)
- [Role-Play with Large Language Models](https://arxiv.org/abs/2305.16367) (Mai 2023)
- [Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations](https://arxiv.org/abs/2305.13299v1) (Mai 2023)
- [Fact-Checking Complex Claims with Program-Guided Reasoning](https://arxiv.org/abs/2305.12744v1) (Mai 2023)
- [Large Language Models as Tool Makers](https://arxiv.org/abs/2305.17126v1) (Mai 2023)
- [Iterative Forward Tuning Boosts In-context Learning in Language Models](https://arxiv.org/abs/2305.13016v2) (Mai 2023)
- [SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks](https://arxiv.org/abs/2305.17390v1) (Mai 2023)
- [Interactive Natural Language Processing](https://arxiv.org/abs/2305.13246v1) (Mai 2023)
- [An automatically discovered chain-of-thought prompt generalizes to novel models and datasets](https://arxiv.org/abs/2305.02897v1) (Mai 2023)
- [Large Language Model Guided Tree-of-Thought](https://arxiv.org/abs/2305.08291v1) (Mai 2023)
- [Active Retrieval Augmented Generation](https://arxiv.org/abs/2305.06983v1) (Mai 2023)
- [A PhD Student's Perspective on Research in NLP in the Era of Very Large Language Models](https://arxiv.org/abs/2305.12544v1) (Mai 2023)
- [Visual Chain of Thought: Bridging Logical Gaps with Multimodal Infillings](https://arxiv.org/abs/2305.02317v1) (Mai 2023)
- [Mirages: On Anthropomorphism in Dialogue Systems](https://arxiv.org/abs/2305.09800v1) (Mai 2023)
- [Model evaluation for extreme risks](https://arxiv.org/abs/2305.15324v1) (Mai 2023)
- [Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting](https://arxiv.org/abs/2305.04388v1) (Mai 2023)
- [Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction](https://arxiv.org/abs/2305.02466v1) (Mai 2023)
- [PromptClass: Weakly-Supervised Text Classification with Prompting Enhanced Noise-Robust Self-Training](https://arxiv.org/abs/2305.13723) (Mai 2023)
- [Augmented Large Language Models with Parametric Knowledge Guiding](https://arxiv.org/abs/2305.04757v2) (Mai 2023)
- [Aligning Large Language Models through Synthetic Feedback](https://arxiv.org/abs/2305.13735) (Mai 2023)
- [Concept-aware Training Improves In-context Learning Ability of Language Models](https://arxiv.org/abs/2305.13775) (Mai 2023)
- [FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance](https://arxiv.org/abs/2305.05176v1) (Mai 2023)
- [Enhancing Black-Box Few-Shot Text Classification with Prompt-Based Data Augmentation](https://arxiv.org/abs/2305.13785) (Mai 2023)
- [Detecting automatically the layout of clinical documents to enhance the performances of downstream natural language processing](https://arxiv.org/abs/2305.13817) (Mai 2023)
- ["Is the Pope Catholic?" Applying Chain-of-Thought Reasoning to Understanding Conversational Implicatures](https://arxiv.org/abs/2305.13826) (Mai 2023)
- [Let's Think Frame by Frame: Evaluating Video Chain of Thought with Video Infilling and Prediction](https://arxiv.org/abs/2305.13903) (Mai 2023)
- [Generating Data for Symbolic Language with Large Language Models](https://arxiv.org/abs/2305.13917) (Mai 2023)
- [Make a Choice! Knowledge Base Question Answering with In-Context Learning](https://arxiv.org/abs/2305.13972) (Mai 2023)
- [Improving Language Models via Plug-and-Play Retrieval Feedback](https://arxiv.org/abs/2305.14002) (Mai 2023)
- [Multi-Granularity Prompts for Topic Shift Detection in Dialogue](https://arxiv.org/abs/2305.14006) (Mai 2023)
- [The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning](https://arxiv.org/abs/2305.14045) (Mai 2023)
- [Can Language Models Understand Physical Concepts?](https://arxiv.org/abs/2305.14057) (Mai 2023)
- [Evaluating Factual Consistency of Summaries with Large Language Models](https://arxiv.org/abs/2305.14069) (Mai 2023)
- [Dr.ICL: Demonstration-Retrieved In-context Learning](https://arxiv.org/abs/2305.14128) (Mai 2023)
- [Probing in Context: Toward Building Robust Classifiers via Probing Large Language Models](https://arxiv.org/abs/2305.14171) (Mai 2023)
- [Skill-Based Few-Shot Selection for In-Context Learning](https://arxiv.org/abs/2305.14210) (Mai 2023)
- [Exploring Chain-of-Thought Style Prompting for Text-to-SQL](https://arxiv.org/abs/2305.14215) (Mai 2023)
- [Enhancing Chat Language Models by Scaling High-quality Instructional Conversations](https://arxiv.org/abs/2305.14233) (Mai 2023)
- [On Learning to Summarize with Large Language Models as References](https://arxiv.org/abs/2305.14239) (Mai 2023)
- [Learning to Generate Novel Scientific Directions with Contextualized Literature-based Discovery](https://arxiv.org/abs/2305.14259) (Mai 2023)
- [Active Learning Principles for In-Context Learning with Large Language Models](https://arxiv.org/abs/2305.14264) (Mai 2023)
- [Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs](https://arxiv.org/abs/2305.14279) (Mai 2023)
- [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://arxiv.org/abs/2305.14325) (Mai 2023)
- [ChatCoT: Tool-Augmented Chain-of-Thought Reasoning on\\ Chat-based Large Language Models](https://arxiv.org/abs/2305.14323) (Mai 2023)
- [WikiChat: A Few-Shot LLM-Based Chatbot Grounded with Wikipedia](https://arxiv.org/abs/2305.14292) (Mai 2023)
- [Query Rewriting for Retrieval-Augmented Large Language Models](https://arxiv.org/abs/2305.14283) (Mai 2023)
- [Discrete Prompt Optimization via Constrained Generation for Zero-shot Re-ranker](https://arxiv.org/abs/2305.13729) (Mai 2023)
- [Element-aware Summarization with Large Language Models: Expert-aligned Evaluation and Chain-of-Thought Method](https://arxiv.org/abs/2305.13412) (Mai 2023)
- [Small Language Models Improve Giants by Rewriting Their Outputs](https://arxiv.org/abs/2305.13514) (Mai 2023)
- [Prompting and Evaluating Large Language Models for Proactive Dialogues: Clarification, Target-guided, and Non-collaboration](https://arxiv.org/abs/2305.13626) (Mai 2023)
- [Prompt-Based Monte-Carlo Tree Search for Goal-Oriented Dialogue Policy Planning](https://arxiv.org/abs/2305.13660) (Mai 2023)
- [Mitigating Language Model Hallucination with Interactive Question-Knowledge Alignment](https://arxiv.org/abs/2305.13669) (Mai 2023)
- [Making Language Models Better Tool Learners with Execution Feedback](https://arxiv.org/abs/2305.13068) (Mai 2023)
- [Text-to-SQL Error Correction with Language Models of Code](https://arxiv.org/abs/2305.13073) (Mai 2023)
- [Decomposed Prompting for Machine Translation Between Related Languages using Large Language Models](https://arxiv.org/abs/2305.13085) (Mai 2023)
- [SPARSEFIT: Few-shot Prompting with Sparse Fine-tuning for Jointly Generating Predictions and Natural Language Explanations](https://arxiv.org/abs/2305.13235) (Mai 2023)
- ["According to ..." Prompting Language Models Improves Quoting from Pre-Training Data](https://arxiv.org/abs/2305.13252) (Mai 2023)
- [Prompt-based methods Mai underestimate large language models' linguistic generalizations](https://arxiv.org/abs/2305.13264) (Mai 2023)
- [Chain of Knowledge: A Framework for Grounding Large Language Models with Structured Knowledge Bases](https://arxiv.org/abs/2305.13269) (Mai 2023)
- [Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations](https://arxiv.org/abs/2305.13299) (Mai 2023)
- [Automated Few-shot Classification with Instruction-Finetuned Language Models](https://arxiv.org/abs/2305.12576) (Mai 2023)
- [Enhancing Few-shot Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategies](https://arxiv.org/abs/2305.12586) (Mai 2023)
- [MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction](https://arxiv.org/abs/2305.12627) (Mai 2023)
- [Learning Interpretable Style Embeddings via Prompting LLMs](https://arxiv.org/abs/2305.12696) (Mai 2023)
- [Enhancing Small Medical Learners with Privacy-preserving Contextual Prompting](https://arxiv.org/abs/2305.12723) (Mai 2023)
- [Fact-Checking Complex Claims with Program-Guided Reasoning](https://arxiv.org/abs/2305.12744) (Mai 2023)
- [A Benchmark on Extremely Weakly Supervised Text Classification: Reconcile Seed Matching and Prompting Approaches](https://arxiv.org/abs/2305.12749) (Mai 2023)
- [This Prompt is Measuring \<MASK\>: Evaluating Bias Evaluation in Language Models](https://arxiv.org/abs/2305.12757) (Mai 2023)
- [Enhancing Cross-lingual Natural Language Inference by Soft Prompting with Multilingual Verbalizer](https://arxiv.org/abs/2305.12761) (Mai 2023)
- [Evaluating Prompt-based Question Answering for Object Prediction in the Open Research Knowledge Graph](https://arxiv.org/abs/2305.12900) (Mai 2023)
- [Explaining How Transformers Use Context to Build Predictions](https://arxiv.org/abs/2305.12535) (Mai 2023)
- [PiVe: Prompting with Iterative Verification Improving Graph-based Generative Capability of LLMs](https://arxiv.org/abs/2305.12392) (Mai 2023)
- [PromptNER: A Prompting Method for Few-shot Named Entity Recognition via k Nearest Neighbor Search](https://arxiv.org/abs/2305.12217) (Mai 2023)
- [Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning](https://arxiv.org/abs/2305.12295) (Mai 2023)
- [Enhancing Few-shot NER with Prompt Ordering based Data Augmentation](https://arxiv.org/abs/2305.11791) (Mai 2023)
- [Chain-of-thought prompting for responding to in-depth dialogue questions with LLM](https://arxiv.org/abs/2305.11792) (Mai 2023)
- [How to Prompt LLMs for Text-to-SQL: A Study in Zero-shot, Single-domain, and Cross-domain Settings](https://arxiv.org/abs/2305.11853) (Mai 2023)
- [Evaluation of medium-large Language Models at zero-shot closed book generative question answering](https://arxiv.org/abs/2305.11991) (Mai 2023)
- [Few-Shot Dialogue Summarization via Skeleton-Assisted Prompt Transfer](https://arxiv.org/abs/2305.12077) (Mai 2023)
- [Can NLP Models Correctly Reason Over Contexts that Break the Common Assumptions?](https://arxiv.org/abs/2305.12096) (Mai 2023)
- [Reasoning Implicit Sentiment with Chain-of-Thought Prompting](https://arxiv.org/abs/2305.11255) (Mai 2023)
- [Writing your own book: A method for going from closed to open book QA to improve robustness and performance of smaller LLMs](https://arxiv.org/abs/2305.11334) (Mai 2023)
- [AutoTrial: Prompting Language Models for Clinical Trial Design](https://arxiv.org/abs/2305.11366) (Mai 2023)
- [CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing](https://arxiv.org/abs/2305.11738) (Mai 2023)
- [Controlling the Extraction of Memorized Data from Large Language Models via Prompt-Tuning](https://arxiv.org/abs/2305.11759) (Mai 2023)
- [Prompting with Pseudo-Code Instructions](https://arxiv.org/abs/2305.11790) (Mai 2023)
- [TrueTeacher: Learning Factual Consistency Evaluation with Large Language Models](https://arxiv.org/abs/2305.11171) (Mai 2023)
- [Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractors](https://arxiv.org/abs/2305.11159) (Mai 2023)
- [Exploiting Biased Models to De-bias Text: A Gender-Fair Rewriting Model](https://arxiv.org/abs/2305.11140) (Mai 2023)
- [Learning In-context Learning for Named Entity Recognition](https://arxiv.org/abs/2305.11038) (Mai 2023)
- [Take a Break in the Middle: Investigating Subgoals towards Hierarchical Script Generation](https://arxiv.org/abs/2305.10907) (Mai 2023)
- [TEPrompt: Task Enlightenment Prompt Learning for Implicit Discourse Relation Recognition](https://arxiv.org/abs/2305.10866) (Mai 2023)
- [Large Language Models can be Guided to Evade AI-Generated Text Detection](https://arxiv.org/abs/2305.10847) (Mai 2023)
- [Temporal Knowledge Graph Forecasting Without Knowledge Using In-Context Learning](https://arxiv.org/abs/2305.10613) (Mai 2023)
- [Prompting the Hidden Talent of Web-Scale Speech Models for Zero-Shot Task Generalization](https://arxiv.org/abs/2305.11095) (Mai 2023)
- [Think Outside the Code: Brainstorming Boosts Large Language Models in Code Generation](https://arxiv.org/abs/2305.10679) (Mai 2023)
- [Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback](https://arxiv.org/abs/2305.10142) (Mai 2023)
- [ConvXAI: Delivering Heterogeneous AI Explanations via Conversations to Support Human-AI Scientific Writing](https://arxiv.org/abs/2305.09770) (Mai 2023)
- [StructGPT: A General Framework for Large Language Model to Reason over Structured Data](https://arxiv.org/abs/2305.09645) (Mai 2023)
- [Towards Expert-Level Medical Question Answering with Large Language Models](https://arxiv.org/abs/2305.09617) (Mai 2023)
- [Large Language Models are Built-in Autoregressive Search Engines](https://arxiv.org/abs/2305.09612) (Mai 2023)
- [MsPrompt: Multi-step Prompt Learning for Debiasing Few-shot Event Detection](https://arxiv.org/abs/2305.09335) (Mai 2023)
- [Exploring the Impact of Layer Normalization for Zero-shot Neural Machine Translation](https://arxiv.org/abs/2305.09312) (Mai 2023)
- [SGP-TOD: Building Task Bots Effortlessly via Schema-Guided LLM Prompting](https://arxiv.org/abs/2305.09067) (Mai 2023)
- [Multi-modal Visual Understanding with Prompts for Semantic Information Disentanglement of Image](https://arxiv.org/abs/2305.09333) (Mai 2023)
- [Soft Prompt Decoding for Multilingual Dense Retrieval](https://arxiv.org/abs/2305.09025) (Mai 2023)
- [PaLM 2 Technical Report](https://ai.google/static/documents/palm2techreport.pdf) (Mai 2023)
- [Are LLMs All You Need for Task-Oriented Dialogue?](https://arxiv.org/abs/2304.06556) (April 2023)
- [HiPrompt: Few-Shot Biomedical Knowledge Fusion via Hierarchy-Oriented Prompting](https://arxiv.org/abs/2304.05973) (April 2023)
- [Approximating Human Evaluation of Social Chatbots with Prompting](https://arxiv.org/abs/2304.05253) (April 2023)
- [Automated Reading Passage Generation with OpenAI's Large Language Model](https://arxiv.org/abs/2304.04616) (April 2023)
- [WebBrain: Learning to Generate Factually Correct Articles for Queries by Grounding on Large Web Corpus](https://arxiv.org/abs/2304.04358) (April 2023)
- [Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition](https://arxiv.org/abs/2304.04704) (April 2023)
- [GPT detectors are biased against non-native English writers](https://arxiv.org/abs/2304.02819) (April 2023)
- [Zero-Shot Next-Item Recommendation using Large Pretrained Language Models](https://arxiv.org/abs/2304.03153) (April 2023)
- [Large Language Models as Master Key: Unlocking the Secrets of Materials Science with GPT](https://arxiv.org/abs/2304.02213) (April 2023)
- [Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-Tuning](https://arxiv.org/abs/2304.01295) (April 2023)
- [Better Language Models of Code through Self-Improvement](https://arxiv.org/abs/2304.01228) (April 2023)
- [PromptORE -- A Novel Approach Towards Fully Unsupervised Relation Extraction](https://arxiv.org/abs/2304.01209) (April 2023)
- [Assessing Language Model Deployment with Risk Cards]() (April 2023)
- [Enhancing Large Language Models with Climate Resources](https://arxiv.org/abs/2304.00116) (März 2023)
- [BloombergGPT: A Large Language Model for Finance](https://arxiv.org/abs/2303.17564) (März 2023)
- [Medical Intervention Duration Estimation Using Language-enhanced Transformer Encoder with Medical Prompts](https://arxiv.org/abs/2303.17408) (März 2023)
- [Soft-prompt tuning to predict lung cancer using primary care free-text Dutch medical notes](https://arxiv.org/abs/2303.15846) (März 2023)
- [TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs](https://arxiv.org/abs/2303.16434) (März 2023)
- [Larger Probes Tell a Different Story: Extending Psycholinguistic Datasets Via In-Context Learning](https://arxiv.org/abs/2303.16445) (März 2023)
- [Linguistically Informed ChatGPT Prompts to Enhance Japanese-Chinese Machine Translation: A Case Study on Attributive Clauses](https://arxiv.org/abs/2303.15587) (März 2023)
- [Knowledge-augmented Frame Semantic Parsing with Hybrid Prompt-tuning](https://arxiv.org/abs/2303.14375) (März 2023)
- [Debiasing Scores and Prompts of 2D Diffusion for Robust Text-to-3D Generation](https://arxiv.org/abs/2303.15413) (März 2023)
- [Zero-shot Model Diagnosis](https://arxiv.org/abs/2303.15441#) (März 2023)
- [Prompting Large Language Models to Generate Code-Mixed Texts: The Case of South East Asian Languages](https://arxiv.org/abs/2303.13592) (März 2023)
- [SPeC: A Soft Prompt-Based Calibration on Mitigating Performance Variability in Clinical Notes Summarization](https://arxiv.org/abs/2303.13035) (März 2023)
- [Large Language Models and Simple, Stupid Bugs](https://arxiv.org/abs/2303.11455) (März 2023)
- [Can Generative Pre-trained Transformers (GPT) Pass Assessments in Higher Education Programming Courses?](https://arxiv.org/abs/2303.09325) (März 2023)
- [SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models](https://arxiv.org/abs/2303.08896) (März 2023)
- [Large Language Models in the Workplace: A Case Study on Prompt Engineering for Job Type Classification](https://arxiv.org/abs/2303.07142) (März 2023)
- [ICL-D3IE: In-Context Learning with Diverse Demonstrations Updating for Document Information Extraction](https://arxiv.org/abs/2303.05063) (März 2023)
- [MathPrompter: Mathematical Reasoning using Large Language Models](https://arxiv.org/abs/2303.05398) (März 2023)
- [Prompt-Based Learning for Thread Structure Prediction in Cybersecurity Forums](https://arxiv.org/abs/2303.05400) (März 2023)
- [Choice Over Control: How Users Write with Large Language Models using Diegetic and Non-Diegetic Prompting](https://arxiv.org/abs/2303.03199) (März 2023)
- [Prompting Large Language Models with Answer Heuristics for Knowledge-based Visual Question Answering](https://arxiv.org/abs/2303.01903) (März 2023)
- [Soft Prompt Guided Joint Learning for Cross-Domain Sentiment Analysis](https://arxiv.org/abs/2303.00815) (März 2023)
- [SpeechPrompt v2: Prompt Tuning for Speech Classification Tasks](https://arxiv.org/abs/2303.00733) (März 2023)
- [Goal Driven Discovery of Distributional Differences via Language Descriptions](https://arxiv.org/abs/2302.14233) (Februar 2023)
- [Navigating the Grey Area: Expressions of Overconfidence and Uncertainty in Language Models](https://arxiv.org/abs/2302.13439) (Februar 2023)
- [TabGenie: A Toolkit for Table-to-Text Generation](https://arxiv.org/abs/2302.14169) (Februar 2023)
- [SGL-PT: A Strong Graph Learner with Graph Prompt Tuning](https://arxiv.org/abs/2302.12449) (Februar 2023)
- [Few-Shot Table-to-Text Generation with Prompt-based Adapter](https://arxiv.org/abs/2302.12468) (Februar 2023)
- [Language Models Are Few-shot Learners for Prognostic Prediction](https://arxiv.org/abs/2302.12692) (Februar 2023)
- [STA: Self-controlled Text Augmentation for Improving Text Classifications](https://arxiv.org/abs/2302.12784) (Februar 2023)
- [Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback](https://arxiv.org/abs/2302.12813) (Februar 2023)
- [How Generative AI models such as ChatGPT can be (Mis)Used in SPC Practice, Education, and Research? An Exploratory Study](https://arxiv.org/abs/2302.10916) (Februar 2023)
- [Grimm in Wonderland: Prompt Engineering with Midjourney to Illustrate Fairytales](https://arxiv.org/abs/2302.08961) (Februar 2023)
- [LabelPrompt: Effective Prompt-based Learning for Relation Classification](https://arxiv.org/abs/2302.08068) (Februar 2023)
- [Language Model Crossover: Variation through Few-Shot Prompting](https://arxiv.org/abs/2302.09236) (Februar 2023)
- [Prompt Tuning of Deep Neural Networks for Speaker-adaptive Visual Speech Recognition](https://arxiv.org/abs/2302.08102) (Februar 2023)
- [The Capacity for Moral Self-Correction in Large Language Models](https://arxiv.org/abs/2302.07459) (Februar 2023)
- [Prompting for Multimodal Hateful Meme Classification](https://arxiv.org/abs/2302.04156) (Februar 2023)
- [PLACES: Prompting Language Models for Social Conversation Synthesis](https://arxiv.org/abs/2302.03269) (Februar 2023)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://arxiv.org/abs/2302.04761) (Februar 2023)
- [Commonsense-Aware Prompting for Controllable Empathetic Dialogue Generation](https://arxiv.org/abs/2302.01441) (Februar 2023)
- [Crawling the Internal Knowledge-Base of Language Models](https://arxiv.org/abs/2301.12810) (Januar 2023)
- [Legal Prompt Engineering for Multilingual Legal Judgement Prediction](https://arxiv.org/abs/2212.02199) (Dezember 2022)
- [Investigating Prompt Engineering in Diffusion Models](https://arxiv.org/abs/2211.15462) (November 2022)
- [Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering](https://arxiv.org/abs/2209.09513v2) (September 2022)
- [Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language](https://arxiv.org/abs/2210.15157) (Oktober 2022)
- [Piloting Copilot and Codex: Hot Temperature, Cold Prompts, or Black Magic?](https://arxiv.org/abs/2210.14699) (Oktober 2022)
- [Plot Writing From Scratch Pre-Trained Language Models](https://aclanthology.org/2022.inlg-main.5) (Juli 2022)
- [Survey of Hallucination in Natural Language Generation](https://arxiv.org/abs/2202.03629) (Februar 2022)

## Sammlungen

- [Chain-of-Thought Papers](https://github.com/Timothyxxx/Chain-of-ThoughtsPapers)
- [Papers mit Code](https://paperswithcode.com/task/prompt-engineering)
- [Prompt Papers](https://github.com/thunlp/PromptPapers#papers)
